Overview

Dataset statistics

Number of variables29
Number of observations85122
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.5 MiB
Average record size in memory264.8 B

Variable types

Numeric12
Categorical17

Alerts

date_x has a high cardinality: 1361 distinct valuesHigh cardinality
id_x is highly overall correlated with country_id and 1 other fieldsHigh correlation
match_api_id is highly overall correlated with seasonHigh correlation
B365H is highly overall correlated with BWH and 5 other fieldsHigh correlation
BWH is highly overall correlated with B365H and 5 other fieldsHigh correlation
IWH is highly overall correlated with B365H and 5 other fieldsHigh correlation
LBH is highly overall correlated with B365H and 5 other fieldsHigh correlation
WHH is highly overall correlated with B365H and 5 other fieldsHigh correlation
VCH is highly overall correlated with B365H and 5 other fieldsHigh correlation
avgOdds is highly overall correlated with B365H and 5 other fieldsHigh correlation
country_id is highly overall correlated with id_x and 1 other fieldsHigh correlation
league_id is highly overall correlated with id_x and 1 other fieldsHigh correlation
season is highly overall correlated with match_api_idHigh correlation
date_y is highly overall correlated with buildUpPlayDribblingClassHigh correlation
buildUpPlayDribblingClass is highly overall correlated with date_yHigh correlation
buildUpPlayPositioningClass is highly imbalanced (61.4%)Imbalance
defencePressureClass is highly imbalanced (55.8%)Imbalance
defenceAggressionClass is highly imbalanced (60.7%)Imbalance
defenceTeamWidthClass is highly imbalanced (64.1%)Imbalance
defenceDefenderLineClass is highly imbalanced (57.3%)Imbalance
homeTeamID is highly skewed (γ1 = 27.14622012)Skewed
away_team_api_id is highly skewed (γ1 = 21.85942501)Skewed

Reproduction

Analysis started2023-02-28 23:51:26.139893
Analysis finished2023-02-28 23:51:54.983176
Duration28.84 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

id_x
Real number (ℝ)

Distinct14503
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10708.051
Minimum1729
Maximum24557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:51:55.062194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1729
5-th percentile2439
Q15312
median8967
Q312652
95-th percentile23815
Maximum24557
Range22828
Interquartile range (IQR)7340

Descriptive statistics

Standard deviation7005.7292
Coefficient of variation (CV)0.65424879
Kurtosis-0.59839634
Mean10708.051
Median Absolute Deviation (MAD)3671
Skewness0.82642879
Sum9.1149069 × 108
Variance49080242
MonotonicityNot monotonic
2023-02-28T18:51:55.185222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1729 6
 
< 0.1%
10476 6
 
< 0.1%
13250 6
 
< 0.1%
13270 6
 
< 0.1%
10263 6
 
< 0.1%
10288 6
 
< 0.1%
10312 6
 
< 0.1%
10331 6
 
< 0.1%
10349 6
 
< 0.1%
10387 6
 
< 0.1%
Other values (14493) 85062
99.9%
ValueCountFrequency (%)
1729 6
< 0.1%
1730 6
< 0.1%
1731 6
< 0.1%
1732 6
< 0.1%
1733 6
< 0.1%
1734 6
< 0.1%
1735 6
< 0.1%
1736 6
< 0.1%
1737 6
< 0.1%
1738 6
< 0.1%
ValueCountFrequency (%)
24557 6
< 0.1%
24556 5
< 0.1%
24555 6
< 0.1%
24554 6
< 0.1%
24553 6
< 0.1%
24552 6
< 0.1%
24551 6
< 0.1%
24550 6
< 0.1%
24549 6
< 0.1%
24548 6
< 0.1%

country_id
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1729
18216 
21518
17786 
4769
17479 
10257
17210 
7809
14431 

Length

Max length5
Median length4
Mean length4.4111276
Min length4

Characters and Unicode

Total characters375484
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1729
2nd row1729
3rd row1729
4th row1729
5th row1729

Common Values

ValueCountFrequency (%)
1729 18216
21.4%
21518 17786
20.9%
4769 17479
20.5%
10257 17210
20.2%
7809 14431
17.0%

Length

2023-02-28T18:51:55.293247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:51:55.404271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1729 18216
21.4%
21518 17786
20.9%
4769 17479
20.5%
10257 17210
20.2%
7809 14431
17.0%

Most occurring characters

ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 375484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 375484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 375484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

league_id
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1729
18216 
21518
17786 
4769
17479 
10257
17210 
7809
14431 

Length

Max length5
Median length4
Mean length4.4111276
Min length4

Characters and Unicode

Total characters375484
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1729
2nd row1729
3rd row1729
4th row1729
5th row1729

Common Values

ValueCountFrequency (%)
1729 18216
21.4%
21518 17786
20.9%
4769 17479
20.5%
10257 17210
20.2%
7809 14431
17.0%

Length

2023-02-28T18:51:55.515298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:51:55.623320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1729 18216
21.4%
21518 17786
20.9%
4769 17479
20.5%
10257 17210
20.2%
7809 14431
17.0%

Most occurring characters

ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 375484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 375484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 375484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

season
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2013/2014
10760 
2010/2011
10753 
2012/2013
10751 
2014/2015
10644 
2011/2012
10640 
Other values (3)
31574 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters766098
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008/2009
2nd row2008/2009
3rd row2008/2009
4th row2008/2009
5th row2008/2009

Common Values

ValueCountFrequency (%)
2013/2014 10760
12.6%
2010/2011 10753
12.6%
2012/2013 10751
12.6%
2014/2015 10644
12.5%
2011/2012 10640
12.5%
2008/2009 10586
12.4%
2009/2010 10575
12.4%
2015/2016 10413
12.2%

Length

2023-02-28T18:51:55.726344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:51:55.843369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2013/2014 10760
12.6%
2010/2011 10753
12.6%
2012/2013 10751
12.6%
2014/2015 10644
12.5%
2011/2012 10640
12.5%
2008/2009 10586
12.4%
2009/2010 10575
12.4%
2015/2016 10413
12.2%

Most occurring characters

ValueCountFrequency (%)
0 223319
29.2%
2 191635
25.0%
1 159890
20.9%
/ 85122
 
11.1%
3 21511
 
2.8%
4 21404
 
2.8%
9 21161
 
2.8%
5 21057
 
2.7%
8 10586
 
1.4%
6 10413
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 680976
88.9%
Other Punctuation 85122
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223319
32.8%
2 191635
28.1%
1 159890
23.5%
3 21511
 
3.2%
4 21404
 
3.1%
9 21161
 
3.1%
5 21057
 
3.1%
8 10586
 
1.6%
6 10413
 
1.5%
Other Punctuation
ValueCountFrequency (%)
/ 85122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 766098
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 223319
29.2%
2 191635
25.0%
1 159890
20.9%
/ 85122
 
11.1%
3 21511
 
2.8%
4 21404
 
2.8%
9 21161
 
2.8%
5 21057
 
2.7%
8 10586
 
1.4%
6 10413
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 766098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 223319
29.2%
2 191635
25.0%
1 159890
20.9%
/ 85122
 
11.1%
3 21511
 
2.8%
4 21404
 
2.8%
9 21161
 
2.8%
5 21057
 
2.7%
8 10586
 
1.4%
6 10413
 
1.4%

date_x
Categorical

Distinct1361
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2012-05-13 00:00:00
 
221
2012-04-07 00:00:00
 
210
2013-03-30 00:00:00
 
208
2015-04-04 00:00:00
 
200
2010-04-03 00:00:00
 
194
Other values (1356)
84089 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1617318
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008-08-17 00:00:00
2nd row2008-08-17 00:00:00
3rd row2008-08-17 00:00:00
4th row2008-08-17 00:00:00
5th row2008-08-17 00:00:00

Common Values

ValueCountFrequency (%)
2012-05-13 00:00:00 221
 
0.3%
2012-04-07 00:00:00 210
 
0.2%
2013-03-30 00:00:00 208
 
0.2%
2015-04-04 00:00:00 200
 
0.2%
2010-04-03 00:00:00 194
 
0.2%
2009-04-11 00:00:00 192
 
0.2%
2011-05-07 00:00:00 191
 
0.2%
2008-10-29 00:00:00 183
 
0.2%
2015-05-23 00:00:00 180
 
0.2%
2011-02-05 00:00:00 180
 
0.2%
Other values (1351) 83163
97.7%

Length

2023-02-28T18:51:55.971398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 85122
50.0%
2012-05-13 221
 
0.1%
2012-04-07 210
 
0.1%
2013-03-30 208
 
0.1%
2015-04-04 200
 
0.1%
2010-04-03 194
 
0.1%
2009-04-11 192
 
0.1%
2011-05-07 191
 
0.1%
2008-10-29 183
 
0.1%
2015-05-23 180
 
0.1%
Other values (1352) 83343
49.0%

Most occurring characters

ValueCountFrequency (%)
0 721808
44.6%
- 170244
 
10.5%
: 170244
 
10.5%
1 161743
 
10.0%
2 152302
 
9.4%
85122
 
5.3%
3 33030
 
2.0%
4 28640
 
1.8%
9 26945
 
1.7%
5 26285
 
1.6%
Other values (3) 40955
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1191708
73.7%
Dash Punctuation 170244
 
10.5%
Other Punctuation 170244
 
10.5%
Space Separator 85122
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 721808
60.6%
1 161743
 
13.6%
2 152302
 
12.8%
3 33030
 
2.8%
4 28640
 
2.4%
9 26945
 
2.3%
5 26285
 
2.2%
8 18871
 
1.6%
6 14029
 
1.2%
7 8055
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 170244
100.0%
Other Punctuation
ValueCountFrequency (%)
: 170244
100.0%
Space Separator
ValueCountFrequency (%)
85122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1617318
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 721808
44.6%
- 170244
 
10.5%
: 170244
 
10.5%
1 161743
 
10.0%
2 152302
 
9.4%
85122
 
5.3%
3 33030
 
2.0%
4 28640
 
1.8%
9 26945
 
1.7%
5 26285
 
1.6%
Other values (3) 40955
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1617318
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 721808
44.6%
- 170244
 
10.5%
: 170244
 
10.5%
1 161743
 
10.0%
2 152302
 
9.4%
85122
 
5.3%
3 33030
 
2.0%
4 28640
 
1.8%
9 26945
 
1.7%
5 26285
 
1.6%
Other values (3) 40955
 
2.5%

match_api_id
Real number (ℝ)

Distinct14503
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1196854.3
Minimum483129
Maximum2118418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:51:56.077422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum483129
5-th percentile489392
Q1829888.25
median1216804
Q31536862
95-th percentile2030088
Maximum2118418
Range1635289
Interquartile range (IQR)706973.75

Descriptive statistics

Standard deviation491767.11
Coefficient of variation (CV)0.41088303
Kurtosis-1.1730164
Mean1196854.3
Median Absolute Deviation (MAD)386875
Skewness0.23275169
Sum1.0187863 × 1011
Variance2.4183489 × 1011
MonotonicityNot monotonic
2023-02-28T18:51:56.208453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
489042 6
 
< 0.1%
539848 6
 
< 0.1%
2060390 6
 
< 0.1%
2060430 6
 
< 0.1%
537638 6
 
< 0.1%
539666 6
 
< 0.1%
539692 6
 
< 0.1%
539711 6
 
< 0.1%
539729 6
 
< 0.1%
539759 6
 
< 0.1%
Other values (14493) 85062
99.9%
ValueCountFrequency (%)
483129 6
< 0.1%
483130 6
< 0.1%
483131 6
< 0.1%
483132 4
< 0.1%
483133 6
< 0.1%
483134 6
< 0.1%
483135 6
< 0.1%
483136 6
< 0.1%
483137 6
< 0.1%
483138 6
< 0.1%
ValueCountFrequency (%)
2118418 6
< 0.1%
2060645 6
< 0.1%
2060644 6
< 0.1%
2060643 6
< 0.1%
2060642 6
< 0.1%
2060641 6
< 0.1%
2060640 6
< 0.1%
2060639 6
< 0.1%
2060638 6
< 0.1%
2060637 6
< 0.1%

homeTeamID
Real number (ℝ)

Distinct164
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9356.3284
Minimum4087
Maximum208931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:51:56.345484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4087
5-th percentile8178
Q18535
median8686
Q39869
95-th percentile10260
Maximum208931
Range204844
Interquartile range (IQR)1334

Descriptive statistics

Standard deviation5639.3222
Coefficient of variation (CV)0.60272812
Kurtosis827.31043
Mean9356.3284
Median Absolute Deviation (MAD)509
Skewness27.14622
Sum7.9642939 × 108
Variance31801955
MonotonicityNot monotonic
2023-02-28T18:51:56.460510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8305 912
 
1.1%
8689 912
 
1.1%
9853 912
 
1.1%
9941 912
 
1.1%
9864 912
 
1.1%
9847 912
 
1.1%
8456 912
 
1.1%
8634 912
 
1.1%
8633 912
 
1.1%
9906 912
 
1.1%
Other values (154) 76002
89.3%
ValueCountFrequency (%)
4087 380
0.4%
4170 57
 
0.1%
6269 68
 
0.1%
6391 38
 
< 0.1%
7794 375
0.4%
7819 564
0.7%
7869 114
 
0.1%
7878 475
0.6%
7943 342
0.4%
8121 114
 
0.1%
ValueCountFrequency (%)
208931 38
 
< 0.1%
108893 114
 
0.1%
10281 456
0.5%
10278 95
 
0.1%
10269 810
1.0%
10268 228
 
0.3%
10267 912
1.1%
10261 798
0.9%
10260 906
1.1%
10252 912
1.1%

away_team_api_id
Real number (ℝ)

Distinct164
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9510.4273
Minimum4087
Maximum208931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:51:56.583540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4087
5-th percentile8178
Q18534
median8686
Q39869
95-th percentile10260
Maximum208931
Range204844
Interquartile range (IQR)1335

Descriptive statistics

Standard deviation8065.376
Coefficient of variation (CV)0.84805611
Kurtosis506.51092
Mean9510.4273
Median Absolute Deviation (MAD)509
Skewness21.859425
Sum8.0954659 × 108
Variance65050290
MonotonicityNot monotonic
2023-02-28T18:51:56.700564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10252 912
 
1.1%
10194 912
 
1.1%
8456 912
 
1.1%
8455 912
 
1.1%
8668 912
 
1.1%
10260 912
 
1.1%
8586 912
 
1.1%
8650 912
 
1.1%
8472 906
 
1.1%
9825 906
 
1.1%
Other values (154) 76014
89.3%
ValueCountFrequency (%)
4087 446
0.5%
4170 108
 
0.1%
6269 104
 
0.1%
6391 110
 
0.1%
7794 439
0.5%
7819 550
0.6%
7869 109
 
0.1%
7878 561
0.7%
7943 329
0.4%
8121 106
 
0.1%
ValueCountFrequency (%)
208931 109
 
0.1%
108893 114
 
0.1%
10281 442
0.5%
10278 113
 
0.1%
10269 803
0.9%
10268 222
 
0.3%
10267 890
1.0%
10261 792
0.9%
10260 912
1.1%
10252 912
1.1%

B365H
Real number (ℝ)

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5767614
Minimum1.04
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:51:56.817590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile1.25
Q11.67
median2.1
Q32.75
95-th percentile5.75
Maximum26
Range24.96
Interquartile range (IQR)1.08

Descriptive statistics

Standard deviation1.7376252
Coefficient of variation (CV)0.67434463
Kurtosis23.325409
Mean2.5767614
Median Absolute Deviation (MAD)0.5
Skewness3.9216386
Sum219339.08
Variance3.0193413
MonotonicityNot monotonic
2023-02-28T18:51:56.937617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 4059
 
4.8%
2 3720
 
4.4%
1.91 2948
 
3.5%
2.5 2919
 
3.4%
2.2 2919
 
3.4%
1.44 2363
 
2.8%
2.4 2337
 
2.7%
2.25 2244
 
2.6%
2.3 2202
 
2.6%
1.8 1947
 
2.3%
Other values (108) 57464
67.5%
ValueCountFrequency (%)
1.04 30
 
< 0.1%
1.05 102
 
0.1%
1.06 126
 
0.1%
1.07 72
 
0.1%
1.08 168
0.2%
1.09 54
 
0.1%
1.1 204
0.2%
1.11 84
 
0.1%
1.13 300
0.4%
1.14 336
0.4%
ValueCountFrequency (%)
26 11
 
< 0.1%
23 5
 
< 0.1%
21 17
 
< 0.1%
19 21
 
< 0.1%
17 41
 
< 0.1%
15 135
0.2%
14 6
 
< 0.1%
13 235
0.3%
12 149
0.2%
11 132
0.2%

BWH
Real number (ℝ)

Distinct220
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5241906
Minimum1.03
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:51:57.056643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.03
5-th percentile1.25
Q11.67
median2.1
Q32.7
95-th percentile5.5
Maximum34
Range32.97
Interquartile range (IQR)1.03

Descriptive statistics

Standard deviation1.6079208
Coefficient of variation (CV)0.63700453
Kurtosis27.662383
Mean2.5241906
Median Absolute Deviation (MAD)0.5
Skewness3.9201569
Sum214864.15
Variance2.5854094
MonotonicityNot monotonic
2023-02-28T18:51:57.177670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 2827
 
3.3%
2.1 2591
 
3.0%
2.05 2554
 
3.0%
1.95 2505
 
2.9%
2.15 2291
 
2.7%
2.25 2167
 
2.5%
2.3 2079
 
2.4%
2.4 2039
 
2.4%
2.2 2037
 
2.4%
1.75 2007
 
2.4%
Other values (210) 62025
72.9%
ValueCountFrequency (%)
1.03 12
 
< 0.1%
1.04 6
 
< 0.1%
1.05 78
 
0.1%
1.06 90
 
0.1%
1.07 72
 
0.1%
1.08 198
0.2%
1.09 90
 
0.1%
1.1 168
0.2%
1.11 126
0.1%
1.12 240
0.3%
ValueCountFrequency (%)
34 6
 
< 0.1%
21 15
< 0.1%
19 6
 
< 0.1%
18 5
 
< 0.1%
17 11
< 0.1%
16.5 7
 
< 0.1%
16 12
< 0.1%
15.5 11
< 0.1%
15 18
< 0.1%
14.5 12
< 0.1%

IWH
Real number (ℝ)

Distinct146
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4392142
Minimum1.05
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:51:57.302698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.05
5-th percentile1.25
Q11.7
median2.1
Q32.6
95-th percentile5.1
Maximum20
Range18.95
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation1.420259
Coefficient of variation (CV)0.58226087
Kurtosis17.938802
Mean2.4392142
Median Absolute Deviation (MAD)0.45
Skewness3.4942613
Sum207630.79
Variance2.0171356
MonotonicityNot monotonic
2023-02-28T18:51:57.421725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 5315
 
6.2%
2.1 4970
 
5.8%
2.2 4647
 
5.5%
1.9 4088
 
4.8%
2.3 3906
 
4.6%
2.4 3219
 
3.8%
1.8 3052
 
3.6%
2.5 2729
 
3.2%
2.6 2629
 
3.1%
1.85 2331
 
2.7%
Other values (136) 48236
56.7%
ValueCountFrequency (%)
1.05 84
 
0.1%
1.07 198
 
0.2%
1.08 48
 
0.1%
1.1 276
0.3%
1.11 42
 
< 0.1%
1.12 360
0.4%
1.13 12
 
< 0.1%
1.15 510
0.6%
1.17 384
0.5%
1.18 42
 
< 0.1%
ValueCountFrequency (%)
20 11
 
< 0.1%
15 19
 
< 0.1%
14 50
0.1%
13 53
0.1%
12.5 21
 
< 0.1%
12 96
0.1%
11.5 12
 
< 0.1%
11 77
0.1%
10.5 30
 
< 0.1%
10.3 50
0.1%

LBH
Real number (ℝ)

Distinct119
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4959032
Minimum1.04
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:51:57.550754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile1.25
Q11.67
median2.1
Q32.62
95-th percentile5.5
Maximum26
Range24.96
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation1.5899034
Coefficient of variation (CV)0.63700525
Kurtosis22.841074
Mean2.4959032
Median Absolute Deviation (MAD)0.48
Skewness3.8426793
Sum212456.27
Variance2.5277929
MonotonicityNot monotonic
2023-02-28T18:51:57.671781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 3977
 
4.7%
2 3850
 
4.5%
2.2 3169
 
3.7%
2.25 2877
 
3.4%
1.8 2764
 
3.2%
2.5 2643
 
3.1%
1.91 2563
 
3.0%
2.4 2043
 
2.4%
1.83 1970
 
2.3%
2.38 1965
 
2.3%
Other values (109) 57301
67.3%
ValueCountFrequency (%)
1.04 18
 
< 0.1%
1.05 72
 
0.1%
1.06 36
 
< 0.1%
1.07 126
0.1%
1.08 150
0.2%
1.09 114
0.1%
1.1 198
0.2%
1.11 114
0.1%
1.12 216
0.3%
1.13 30
 
< 0.1%
ValueCountFrequency (%)
26 6
 
< 0.1%
23 5
 
< 0.1%
21 6
 
< 0.1%
19 14
 
< 0.1%
17 23
 
< 0.1%
15 79
0.1%
13 151
0.2%
12 166
0.2%
11 132
0.2%
10.5 6
 
< 0.1%

WHH
Real number (ℝ)

Distinct116
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5517918
Minimum1.02
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:51:57.790809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.02
5-th percentile1.25
Q11.7
median2.1
Q32.7
95-th percentile5.5
Maximum26
Range24.98
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6711369
Coefficient of variation (CV)0.65488763
Kurtosis23.643049
Mean2.5517918
Median Absolute Deviation (MAD)0.5
Skewness3.9555182
Sum217213.62
Variance2.7926984
MonotonicityNot monotonic
2023-02-28T18:51:57.912835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 3090
 
3.6%
2 3047
 
3.6%
2.1 2958
 
3.5%
2.3 2868
 
3.4%
2.4 2837
 
3.3%
2.2 2569
 
3.0%
1.91 2564
 
3.0%
2.05 2315
 
2.7%
2.25 2216
 
2.6%
1.44 2207
 
2.6%
Other values (106) 58451
68.7%
ValueCountFrequency (%)
1.02 6
 
< 0.1%
1.04 12
 
< 0.1%
1.05 54
 
0.1%
1.06 84
 
0.1%
1.07 42
 
< 0.1%
1.08 216
0.3%
1.1 270
0.3%
1.11 132
 
0.2%
1.12 156
 
0.2%
1.14 432
0.5%
ValueCountFrequency (%)
26 6
 
< 0.1%
23 5
 
< 0.1%
21 19
 
< 0.1%
19 11
 
< 0.1%
17 42
 
< 0.1%
15 146
0.2%
13 108
0.1%
12 219
0.3%
11 184
0.2%
10.5 12
 
< 0.1%

VCH
Real number (ℝ)

Distinct156
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6233912
Minimum1.03
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:51:58.038864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.03
5-th percentile1.25
Q11.7
median2.1
Q32.75
95-th percentile5.75
Maximum36
Range34.97
Interquartile range (IQR)1.05

Descriptive statistics

Standard deviation1.8911879
Coefficient of variation (CV)0.72089435
Kurtosis35.770315
Mean2.6233912
Median Absolute Deviation (MAD)0.5
Skewness4.6598604
Sum223308.3
Variance3.5765916
MonotonicityNot monotonic
2023-02-28T18:51:58.422951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 3205
 
3.8%
2 3035
 
3.6%
2.2 2755
 
3.2%
2.3 2346
 
2.8%
2.5 2341
 
2.8%
2.05 2320
 
2.7%
2.25 2194
 
2.6%
2.4 1803
 
2.1%
1.8 1795
 
2.1%
2.15 1787
 
2.1%
Other values (146) 61541
72.3%
ValueCountFrequency (%)
1.03 12
 
< 0.1%
1.04 30
 
< 0.1%
1.05 36
 
< 0.1%
1.06 174
0.2%
1.07 78
0.1%
1.08 54
 
0.1%
1.083 6
 
< 0.1%
1.09 138
0.2%
1.1 156
0.2%
1.11 108
0.1%
ValueCountFrequency (%)
36 6
 
< 0.1%
31 5
 
< 0.1%
29 11
 
< 0.1%
26 2
 
< 0.1%
23 11
 
< 0.1%
22 6
 
< 0.1%
21 23
< 0.1%
20 13
 
< 0.1%
19 41
< 0.1%
18 38
< 0.1%

avgOdds
Real number (ℝ)

Distinct2266
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5268982
Minimum1.042
Maximum28.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:51:58.548979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.042
5-th percentile1.258
Q11.682
median2.1
Q32.684
95-th percentile5.52
Maximum28.4
Range27.358
Interquartile range (IQR)1.002

Descriptive statistics

Standard deviation1.6247736
Coefficient of variation (CV)0.64299131
Kurtosis23.844992
Mean2.5268982
Median Absolute Deviation (MAD)0.478
Skewness3.8963364
Sum215094.63
Variance2.6398891
MonotonicityNot monotonic
2023-02-28T18:51:58.665004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.04 526
 
0.6%
2.05 515
 
0.6%
2.21 485
 
0.6%
2.2 474
 
0.6%
2.15 463
 
0.5%
2.12 457
 
0.5%
2.16 436
 
0.5%
2.13 420
 
0.5%
2.1 409
 
0.5%
2.09 403
 
0.5%
Other values (2256) 80534
94.6%
ValueCountFrequency (%)
1.042 12
< 0.1%
1.044 6
< 0.1%
1.048 6
< 0.1%
1.05 6
< 0.1%
1.052 12
< 0.1%
1.054 6
< 0.1%
1.054 6
< 0.1%
1.056 6
< 0.1%
1.056 6
< 0.1%
1.058 6
< 0.1%
ValueCountFrequency (%)
28.4 6
< 0.1%
22.4 5
< 0.1%
20.8 5
< 0.1%
20.4 6
< 0.1%
19.5 2
 
< 0.1%
19.4 6
< 0.1%
18.4 5
< 0.1%
17.1 5
< 0.1%
17 6
< 0.1%
16.7 6
< 0.1%

id_y
Real number (ℝ)

Distinct924
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean733.76216
Minimum10
Maximum1450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:51:58.784031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile85
Q1347
median777
Q31119
95-th percentile1376
Maximum1450
Range1440
Interquartile range (IQR)772

Descriptive statistics

Standard deviation424.13519
Coefficient of variation (CV)0.57802815
Kurtosis-1.2667832
Mean733.76216
Median Absolute Deviation (MAD)374
Skewness-0.055996648
Sum62459303
Variance179890.66
MonotonicityNot monotonic
2023-02-28T18:51:58.900057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
503 152
 
0.2%
1282 152
 
0.2%
504 152
 
0.2%
1311 152
 
0.2%
801 152
 
0.2%
802 152
 
0.2%
803 152
 
0.2%
804 152
 
0.2%
805 152
 
0.2%
806 152
 
0.2%
Other values (914) 83602
98.2%
ValueCountFrequency (%)
10 56
 
0.1%
11 56
 
0.1%
12 56
 
0.1%
13 56
 
0.1%
14 56
 
0.1%
15 56
 
0.1%
16 150
0.2%
17 150
0.2%
18 150
0.2%
19 150
0.2%
ValueCountFrequency (%)
1450 76
0.1%
1449 76
0.1%
1448 76
0.1%
1447 76
0.1%
1446 76
0.1%
1445 76
0.1%
1433 19
 
< 0.1%
1432 19
 
< 0.1%
1431 19
 
< 0.1%
1430 19
 
< 0.1%

date_y
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2012-02-22 00:00:00
14317 
2013-09-20 00:00:00
14227 
2011-02-22 00:00:00
14204 
2014-09-19 00:00:00
14204 
2015-09-10 00:00:00
14171 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1617318
Distinct characters10
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010-02-22 00:00:00
2nd row2011-02-22 00:00:00
3rd row2012-02-22 00:00:00
4th row2013-09-20 00:00:00
5th row2014-09-19 00:00:00

Common Values

ValueCountFrequency (%)
2012-02-22 00:00:00 14317
16.8%
2013-09-20 00:00:00 14227
16.7%
2011-02-22 00:00:00 14204
16.7%
2014-09-19 00:00:00 14204
16.7%
2015-09-10 00:00:00 14171
16.6%
2010-02-22 00:00:00 13999
16.4%

Length

2023-02-28T18:51:59.008082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:51:59.116106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00 85122
50.0%
2012-02-22 14317
 
8.4%
2013-09-20 14227
 
8.4%
2011-02-22 14204
 
8.3%
2014-09-19 14204
 
8.3%
2015-09-10 14171
 
8.3%
2010-02-22 13999
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0 723373
44.7%
2 241226
 
14.9%
- 170244
 
10.5%
: 170244
 
10.5%
1 127701
 
7.9%
85122
 
5.3%
9 56806
 
3.5%
3 14227
 
0.9%
4 14204
 
0.9%
5 14171
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1191708
73.7%
Dash Punctuation 170244
 
10.5%
Other Punctuation 170244
 
10.5%
Space Separator 85122
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 723373
60.7%
2 241226
 
20.2%
1 127701
 
10.7%
9 56806
 
4.8%
3 14227
 
1.2%
4 14204
 
1.2%
5 14171
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 170244
100.0%
Other Punctuation
ValueCountFrequency (%)
: 170244
100.0%
Space Separator
ValueCountFrequency (%)
85122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1617318
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 723373
44.7%
2 241226
 
14.9%
- 170244
 
10.5%
: 170244
 
10.5%
1 127701
 
7.9%
85122
 
5.3%
9 56806
 
3.5%
3 14227
 
0.9%
4 14204
 
0.9%
5 14171
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1617318
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 723373
44.7%
2 241226
 
14.9%
- 170244
 
10.5%
: 170244
 
10.5%
1 127701
 
7.9%
85122
 
5.3%
9 56806
 
3.5%
3 14227
 
0.9%
4 14204
 
0.9%
5 14171
 
0.9%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Balanced
69579 
Fast
11598 
Slow
 
3945

Length

Max length8
Median length8
Mean length7.269613
Min length4

Characters and Unicode

Total characters618804
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFast
2nd rowBalanced
3rd rowBalanced
4th rowBalanced
5th rowBalanced

Common Values

ValueCountFrequency (%)
Balanced 69579
81.7%
Fast 11598
 
13.6%
Slow 3945
 
4.6%

Length

2023-02-28T18:51:59.240135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:51:59.352159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
balanced 69579
81.7%
fast 11598
 
13.6%
slow 3945
 
4.6%

Most occurring characters

ValueCountFrequency (%)
a 150756
24.4%
l 73524
11.9%
B 69579
11.2%
n 69579
11.2%
c 69579
11.2%
e 69579
11.2%
d 69579
11.2%
F 11598
 
1.9%
s 11598
 
1.9%
t 11598
 
1.9%
Other values (3) 11835
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 533682
86.2%
Uppercase Letter 85122
 
13.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 150756
28.2%
l 73524
13.8%
n 69579
13.0%
c 69579
13.0%
e 69579
13.0%
d 69579
13.0%
s 11598
 
2.2%
t 11598
 
2.2%
o 3945
 
0.7%
w 3945
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
B 69579
81.7%
F 11598
 
13.6%
S 3945
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 618804
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 150756
24.4%
l 73524
11.9%
B 69579
11.2%
n 69579
11.2%
c 69579
11.2%
e 69579
11.2%
d 69579
11.2%
F 11598
 
1.9%
s 11598
 
1.9%
t 11598
 
1.9%
Other values (3) 11835
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 618804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 150756
24.4%
l 73524
11.9%
B 69579
11.2%
n 69579
11.2%
c 69579
11.2%
e 69579
11.2%
d 69579
11.2%
F 11598
 
1.9%
s 11598
 
1.9%
t 11598
 
1.9%
Other values (3) 11835
 
1.9%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Little
59039 
Normal
24046 
Lots
 
2037

Length

Max length6
Median length6
Mean length5.9521393
Min length4

Characters and Unicode

Total characters506658
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLittle
2nd rowLittle
3rd rowLittle
4th rowLittle
5th rowNormal

Common Values

ValueCountFrequency (%)
Little 59039
69.4%
Normal 24046
28.2%
Lots 2037
 
2.4%

Length

2023-02-28T18:51:59.447180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:51:59.555205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
little 59039
69.4%
normal 24046
28.2%
lots 2037
 
2.4%

Most occurring characters

ValueCountFrequency (%)
t 120115
23.7%
l 83085
16.4%
L 61076
12.1%
i 59039
11.7%
e 59039
11.7%
o 26083
 
5.1%
N 24046
 
4.7%
r 24046
 
4.7%
m 24046
 
4.7%
a 24046
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 421536
83.2%
Uppercase Letter 85122
 
16.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 120115
28.5%
l 83085
19.7%
i 59039
14.0%
e 59039
14.0%
o 26083
 
6.2%
r 24046
 
5.7%
m 24046
 
5.7%
a 24046
 
5.7%
s 2037
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
L 61076
71.8%
N 24046
 
28.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 506658
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 120115
23.7%
l 83085
16.4%
L 61076
12.1%
i 59039
11.7%
e 59039
11.7%
o 26083
 
5.1%
N 24046
 
4.7%
r 24046
 
4.7%
m 24046
 
4.7%
a 24046
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 506658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 120115
23.7%
l 83085
16.4%
L 61076
12.1%
i 59039
11.7%
e 59039
11.7%
o 26083
 
5.1%
N 24046
 
4.7%
r 24046
 
4.7%
m 24046
 
4.7%
a 24046
 
4.7%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Mixed
70922 
Short
8696 
Long
 
5504

Length

Max length5
Median length5
Mean length4.9353399
Min length4

Characters and Unicode

Total characters420106
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMixed
2nd rowMixed
3rd rowMixed
4th rowMixed
5th rowMixed

Common Values

ValueCountFrequency (%)
Mixed 70922
83.3%
Short 8696
 
10.2%
Long 5504
 
6.5%

Length

2023-02-28T18:51:59.643224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:51:59.740246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
mixed 70922
83.3%
short 8696
 
10.2%
long 5504
 
6.5%

Most occurring characters

ValueCountFrequency (%)
M 70922
16.9%
i 70922
16.9%
x 70922
16.9%
e 70922
16.9%
d 70922
16.9%
o 14200
 
3.4%
S 8696
 
2.1%
h 8696
 
2.1%
r 8696
 
2.1%
t 8696
 
2.1%
Other values (3) 16512
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 334984
79.7%
Uppercase Letter 85122
 
20.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 70922
21.2%
x 70922
21.2%
e 70922
21.2%
d 70922
21.2%
o 14200
 
4.2%
h 8696
 
2.6%
r 8696
 
2.6%
t 8696
 
2.6%
n 5504
 
1.6%
g 5504
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
M 70922
83.3%
S 8696
 
10.2%
L 5504
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 420106
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 70922
16.9%
i 70922
16.9%
x 70922
16.9%
e 70922
16.9%
d 70922
16.9%
o 14200
 
3.4%
S 8696
 
2.1%
h 8696
 
2.1%
r 8696
 
2.1%
t 8696
 
2.1%
Other values (3) 16512
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 420106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 70922
16.9%
i 70922
16.9%
x 70922
16.9%
e 70922
16.9%
d 70922
16.9%
o 14200
 
3.4%
S 8696
 
2.1%
h 8696
 
2.1%
r 8696
 
2.1%
t 8696
 
2.1%
Other values (3) 16512
 
3.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Organised
78701 
Free Form
 
6421

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters766098
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrganised
2nd rowOrganised
3rd rowOrganised
4th rowOrganised
5th rowOrganised

Common Values

ValueCountFrequency (%)
Organised 78701
92.5%
Free Form 6421
 
7.5%

Length

2023-02-28T18:51:59.822265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:51:59.917287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
organised 78701
86.0%
free 6421
 
7.0%
form 6421
 
7.0%

Most occurring characters

ValueCountFrequency (%)
r 91543
11.9%
e 91543
11.9%
O 78701
10.3%
g 78701
10.3%
a 78701
10.3%
n 78701
10.3%
i 78701
10.3%
s 78701
10.3%
d 78701
10.3%
F 12842
 
1.7%
Other values (3) 19263
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 668134
87.2%
Uppercase Letter 91543
 
11.9%
Space Separator 6421
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 91543
13.7%
e 91543
13.7%
g 78701
11.8%
a 78701
11.8%
n 78701
11.8%
i 78701
11.8%
s 78701
11.8%
d 78701
11.8%
o 6421
 
1.0%
m 6421
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
O 78701
86.0%
F 12842
 
14.0%
Space Separator
ValueCountFrequency (%)
6421
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 759677
99.2%
Common 6421
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 91543
12.1%
e 91543
12.1%
O 78701
10.4%
g 78701
10.4%
a 78701
10.4%
n 78701
10.4%
i 78701
10.4%
s 78701
10.4%
d 78701
10.4%
F 12842
 
1.7%
Other values (2) 12842
 
1.7%
Common
ValueCountFrequency (%)
6421
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 766098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 91543
11.9%
e 91543
11.9%
O 78701
10.3%
g 78701
10.3%
a 78701
10.3%
n 78701
10.3%
i 78701
10.3%
s 78701
10.3%
d 78701
10.3%
F 12842
 
1.7%
Other values (3) 19263
 
2.5%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Normal
68785 
Risky
12948 
Safe
 
3389

Length

Max length6
Median length6
Mean length5.768262
Min length4

Characters and Unicode

Total characters491006
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 68785
80.8%
Risky 12948
 
15.2%
Safe 3389
 
4.0%

Length

2023-02-28T18:52:00.009308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:52:00.117332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 68785
80.8%
risky 12948
 
15.2%
safe 3389
 
4.0%

Most occurring characters

ValueCountFrequency (%)
a 72174
14.7%
N 68785
14.0%
o 68785
14.0%
r 68785
14.0%
m 68785
14.0%
l 68785
14.0%
R 12948
 
2.6%
i 12948
 
2.6%
s 12948
 
2.6%
k 12948
 
2.6%
Other values (4) 23115
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 405884
82.7%
Uppercase Letter 85122
 
17.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 72174
17.8%
o 68785
16.9%
r 68785
16.9%
m 68785
16.9%
l 68785
16.9%
i 12948
 
3.2%
s 12948
 
3.2%
k 12948
 
3.2%
y 12948
 
3.2%
f 3389
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
N 68785
80.8%
R 12948
 
15.2%
S 3389
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 491006
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 72174
14.7%
N 68785
14.0%
o 68785
14.0%
r 68785
14.0%
m 68785
14.0%
l 68785
14.0%
R 12948
 
2.6%
i 12948
 
2.6%
s 12948
 
2.6%
k 12948
 
2.6%
Other values (4) 23115
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 72174
14.7%
N 68785
14.0%
o 68785
14.0%
r 68785
14.0%
m 68785
14.0%
l 68785
14.0%
R 12948
 
2.6%
i 12948
 
2.6%
s 12948
 
2.6%
k 12948
 
2.6%
Other values (4) 23115
 
4.7%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Normal
68412 
Lots
13573 
Little
 
3137

Length

Max length6
Median length6
Mean length5.681093
Min length4

Characters and Unicode

Total characters483586
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLots
2nd rowNormal
3rd rowNormal
4th rowLots
5th rowLots

Common Values

ValueCountFrequency (%)
Normal 68412
80.4%
Lots 13573
 
15.9%
Little 3137
 
3.7%

Length

2023-02-28T18:52:00.212353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:52:00.320378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 68412
80.4%
lots 13573
 
15.9%
little 3137
 
3.7%

Most occurring characters

ValueCountFrequency (%)
o 81985
17.0%
l 71549
14.8%
N 68412
14.1%
r 68412
14.1%
m 68412
14.1%
a 68412
14.1%
t 19847
 
4.1%
L 16710
 
3.5%
s 13573
 
2.8%
i 3137
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 398464
82.4%
Uppercase Letter 85122
 
17.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 81985
20.6%
l 71549
18.0%
r 68412
17.2%
m 68412
17.2%
a 68412
17.2%
t 19847
 
5.0%
s 13573
 
3.4%
i 3137
 
0.8%
e 3137
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
N 68412
80.4%
L 16710
 
19.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 483586
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 81985
17.0%
l 71549
14.8%
N 68412
14.1%
r 68412
14.1%
m 68412
14.1%
a 68412
14.1%
t 19847
 
4.1%
L 16710
 
3.5%
s 13573
 
2.8%
i 3137
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 81985
17.0%
l 71549
14.8%
N 68412
14.1%
r 68412
14.1%
m 68412
14.1%
a 68412
14.1%
t 19847
 
4.1%
L 16710
 
3.5%
s 13573
 
2.8%
i 3137
 
0.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Normal
68866 
Lots
14227 
Little
 
2029

Length

Max length6
Median length6
Mean length5.6657268
Min length4

Characters and Unicode

Total characters482278
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowLots
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 68866
80.9%
Lots 14227
 
16.7%
Little 2029
 
2.4%

Length

2023-02-28T18:52:00.417400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:52:00.528425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 68866
80.9%
lots 14227
 
16.7%
little 2029
 
2.4%

Most occurring characters

ValueCountFrequency (%)
o 83093
17.2%
l 70895
14.7%
N 68866
14.3%
r 68866
14.3%
m 68866
14.3%
a 68866
14.3%
t 18285
 
3.8%
L 16256
 
3.4%
s 14227
 
2.9%
i 2029
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 397156
82.4%
Uppercase Letter 85122
 
17.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 83093
20.9%
l 70895
17.9%
r 68866
17.3%
m 68866
17.3%
a 68866
17.3%
t 18285
 
4.6%
s 14227
 
3.6%
i 2029
 
0.5%
e 2029
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 68866
80.9%
L 16256
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 482278
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 83093
17.2%
l 70895
14.7%
N 68866
14.3%
r 68866
14.3%
m 68866
14.3%
a 68866
14.3%
t 18285
 
3.8%
L 16256
 
3.4%
s 14227
 
2.9%
i 2029
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 482278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 83093
17.2%
l 70895
14.7%
N 68866
14.3%
r 68866
14.3%
m 68866
14.3%
a 68866
14.3%
t 18285
 
3.8%
L 16256
 
3.4%
s 14227
 
2.9%
i 2029
 
0.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Organised
70834 
Free Form
14288 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters766098
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFree Form
2nd rowFree Form
3rd rowOrganised
4th rowOrganised
5th rowOrganised

Common Values

ValueCountFrequency (%)
Organised 70834
83.2%
Free Form 14288
 
16.8%

Length

2023-02-28T18:52:00.614444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:52:00.707465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
organised 70834
71.3%
free 14288
 
14.4%
form 14288
 
14.4%

Most occurring characters

ValueCountFrequency (%)
r 99410
13.0%
e 99410
13.0%
O 70834
9.2%
g 70834
9.2%
a 70834
9.2%
n 70834
9.2%
i 70834
9.2%
s 70834
9.2%
d 70834
9.2%
F 28576
 
3.7%
Other values (3) 42864
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 652400
85.2%
Uppercase Letter 99410
 
13.0%
Space Separator 14288
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 99410
15.2%
e 99410
15.2%
g 70834
10.9%
a 70834
10.9%
n 70834
10.9%
i 70834
10.9%
s 70834
10.9%
d 70834
10.9%
o 14288
 
2.2%
m 14288
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
O 70834
71.3%
F 28576
28.7%
Space Separator
ValueCountFrequency (%)
14288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 751810
98.1%
Common 14288
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 99410
13.2%
e 99410
13.2%
O 70834
9.4%
g 70834
9.4%
a 70834
9.4%
n 70834
9.4%
i 70834
9.4%
s 70834
9.4%
d 70834
9.4%
F 28576
 
3.8%
Other values (2) 28576
 
3.8%
Common
ValueCountFrequency (%)
14288
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 766098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 99410
13.0%
e 99410
13.0%
O 70834
9.2%
g 70834
9.2%
a 70834
9.2%
n 70834
9.2%
i 70834
9.2%
s 70834
9.2%
d 70834
9.2%
F 28576
 
3.7%
Other values (3) 42864
5.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Medium
73587 
Deep
 
7307
High
 
4228

Length

Max length6
Median length6
Mean length5.7289772
Min length4

Characters and Unicode

Total characters487662
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 73587
86.4%
Deep 7307
 
8.6%
High 4228
 
5.0%

Length

2023-02-28T18:52:00.801486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:52:00.911511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
medium 73587
86.4%
deep 7307
 
8.6%
high 4228
 
5.0%

Most occurring characters

ValueCountFrequency (%)
e 88201
18.1%
i 77815
16.0%
M 73587
15.1%
d 73587
15.1%
u 73587
15.1%
m 73587
15.1%
D 7307
 
1.5%
p 7307
 
1.5%
H 4228
 
0.9%
g 4228
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 402540
82.5%
Uppercase Letter 85122
 
17.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 88201
21.9%
i 77815
19.3%
d 73587
18.3%
u 73587
18.3%
m 73587
18.3%
p 7307
 
1.8%
g 4228
 
1.1%
h 4228
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
M 73587
86.4%
D 7307
 
8.6%
H 4228
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 487662
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 88201
18.1%
i 77815
16.0%
M 73587
15.1%
d 73587
15.1%
u 73587
15.1%
m 73587
15.1%
D 7307
 
1.5%
p 7307
 
1.5%
H 4228
 
0.9%
g 4228
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 487662
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 88201
18.1%
i 77815
16.0%
M 73587
15.1%
d 73587
15.1%
u 73587
15.1%
m 73587
15.1%
D 7307
 
1.5%
p 7307
 
1.5%
H 4228
 
0.9%
g 4228
 
0.9%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Press
75305 
Double
 
6399
Contain
 
3418

Length

Max length7
Median length5
Mean length5.1554827
Min length5

Characters and Unicode

Total characters438845
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPress
2nd rowPress
3rd rowPress
4th rowPress
5th rowPress

Common Values

ValueCountFrequency (%)
Press 75305
88.5%
Double 6399
 
7.5%
Contain 3418
 
4.0%

Length

2023-02-28T18:52:01.008533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:52:01.120558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
press 75305
88.5%
double 6399
 
7.5%
contain 3418
 
4.0%

Most occurring characters

ValueCountFrequency (%)
s 150610
34.3%
e 81704
18.6%
P 75305
17.2%
r 75305
17.2%
o 9817
 
2.2%
n 6836
 
1.6%
D 6399
 
1.5%
u 6399
 
1.5%
b 6399
 
1.5%
l 6399
 
1.5%
Other values (4) 13672
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 353723
80.6%
Uppercase Letter 85122
 
19.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 150610
42.6%
e 81704
23.1%
r 75305
21.3%
o 9817
 
2.8%
n 6836
 
1.9%
u 6399
 
1.8%
b 6399
 
1.8%
l 6399
 
1.8%
t 3418
 
1.0%
a 3418
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
P 75305
88.5%
D 6399
 
7.5%
C 3418
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 438845
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 150610
34.3%
e 81704
18.6%
P 75305
17.2%
r 75305
17.2%
o 9817
 
2.2%
n 6836
 
1.6%
D 6399
 
1.5%
u 6399
 
1.5%
b 6399
 
1.5%
l 6399
 
1.5%
Other values (4) 13672
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 438845
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 150610
34.3%
e 81704
18.6%
P 75305
17.2%
r 75305
17.2%
o 9817
 
2.2%
n 6836
 
1.6%
D 6399
 
1.5%
u 6399
 
1.5%
b 6399
 
1.5%
l 6399
 
1.5%
Other values (4) 13672
 
3.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Normal
76062 
Wide
 
7084
Narrow
 
1976

Length

Max length6
Median length6
Mean length5.8335565
Min length4

Characters and Unicode

Total characters496564
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 76062
89.4%
Wide 7084
 
8.3%
Narrow 1976
 
2.3%

Length

2023-02-28T18:52:01.211578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:52:01.317602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 76062
89.4%
wide 7084
 
8.3%
narrow 1976
 
2.3%

Most occurring characters

ValueCountFrequency (%)
r 80014
16.1%
N 78038
15.7%
o 78038
15.7%
a 78038
15.7%
m 76062
15.3%
l 76062
15.3%
W 7084
 
1.4%
i 7084
 
1.4%
d 7084
 
1.4%
e 7084
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 411442
82.9%
Uppercase Letter 85122
 
17.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 80014
19.4%
o 78038
19.0%
a 78038
19.0%
m 76062
18.5%
l 76062
18.5%
i 7084
 
1.7%
d 7084
 
1.7%
e 7084
 
1.7%
w 1976
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 78038
91.7%
W 7084
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 496564
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 80014
16.1%
N 78038
15.7%
o 78038
15.7%
a 78038
15.7%
m 76062
15.3%
l 76062
15.3%
W 7084
 
1.4%
i 7084
 
1.4%
d 7084
 
1.4%
e 7084
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 496564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 80014
16.1%
N 78038
15.7%
o 78038
15.7%
a 78038
15.7%
m 76062
15.3%
l 76062
15.3%
W 7084
 
1.4%
i 7084
 
1.4%
d 7084
 
1.4%
e 7084
 
1.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Cover
77694 
Offside Trap
 
7428

Length

Max length12
Median length5
Mean length5.6108409
Min length5

Characters and Unicode

Total characters477606
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCover
2nd rowCover
3rd rowCover
4th rowCover
5th rowCover

Common Values

ValueCountFrequency (%)
Cover 77694
91.3%
Offside Trap 7428
 
8.7%

Length

2023-02-28T18:52:01.408623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:52:01.507645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
cover 77694
83.9%
offside 7428
 
8.0%
trap 7428
 
8.0%

Most occurring characters

ValueCountFrequency (%)
e 85122
17.8%
r 85122
17.8%
C 77694
16.3%
o 77694
16.3%
v 77694
16.3%
f 14856
 
3.1%
O 7428
 
1.6%
s 7428
 
1.6%
i 7428
 
1.6%
d 7428
 
1.6%
Other values (4) 29712
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 377628
79.1%
Uppercase Letter 92550
 
19.4%
Space Separator 7428
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 85122
22.5%
r 85122
22.5%
o 77694
20.6%
v 77694
20.6%
f 14856
 
3.9%
s 7428
 
2.0%
i 7428
 
2.0%
d 7428
 
2.0%
a 7428
 
2.0%
p 7428
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
C 77694
83.9%
O 7428
 
8.0%
T 7428
 
8.0%
Space Separator
ValueCountFrequency (%)
7428
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 470178
98.4%
Common 7428
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 85122
18.1%
r 85122
18.1%
C 77694
16.5%
o 77694
16.5%
v 77694
16.5%
f 14856
 
3.2%
O 7428
 
1.6%
s 7428
 
1.6%
i 7428
 
1.6%
d 7428
 
1.6%
Other values (3) 22284
 
4.7%
Common
ValueCountFrequency (%)
7428
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 477606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 85122
17.8%
r 85122
17.8%
C 77694
16.3%
o 77694
16.3%
v 77694
16.3%
f 14856
 
3.1%
O 7428
 
1.6%
s 7428
 
1.6%
i 7428
 
1.6%
d 7428
 
1.6%
Other values (4) 29712
 
6.2%

Interactions

2023-02-28T18:51:51.946472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:33.839466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:35.313814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:36.883168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:38.351497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:41.012096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:42.460422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:44.022474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:45.737691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:47.209044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:48.703392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:50.207082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:52.076502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:33.957493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:35.442843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:36.999194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:38.469524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:41.127122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:42.591451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:44.146502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:45.852716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:47.324070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:48.825766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:50.328110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:52.213544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:34.089528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:35.583875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:37.131223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:38.602554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:41.257153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:42.734851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:44.289890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:45.981756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:47.457111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:48.962801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:50.463139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:52.335572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:34.209555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:35.710904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:37.247249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:38.721581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:41.373177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:42.865737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:44.410918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:46.098782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:47.576137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:49.085829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:50.589167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:52.461600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:34.328582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:35.836931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:37.364275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:38.840607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:41.490204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:42.991242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:44.534945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:46.215818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:47.705167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:49.206856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:50.717196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:52.595639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:34.446618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:35.964961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:37.482302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:40.146903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:41.608230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:43.115270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:44.659983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:46.336848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:47.826194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:49.331885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:50.840224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:52.726669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:34.574659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:36.097991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:37.612332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:40.275931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:41.732258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:43.245299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:44.794478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:46.464877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:47.952223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:49.460913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:50.971253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:52.864700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:34.700688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:36.234022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:37.738360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:40.402959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:41.858287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:43.376329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:44.925508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:46.595906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:48.085253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:49.588942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:51.102283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:52.987727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:34.821703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:36.361050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:37.857387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:40.520985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:41.975313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:43.505358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:45.050537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:46.713933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:48.203279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:49.708969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:51.223310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:53.113756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:34.938730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:36.488078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:37.975413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:40.638012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:42.095340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:43.631386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:45.338600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:46.831959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:48.327306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:49.828997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:51.343338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:53.252787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:35.062758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:36.620108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:38.104442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:40.762040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:42.219368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:43.765416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:45.476632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:46.955987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:48.459337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:49.957036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:51.676412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:53.376815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:35.184785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:36.749137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:38.224469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:40.881067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:42.337394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:43.891444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:45.602660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:47.079030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:48.579363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:50.078052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:51:51.810441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-28T18:52:01.612669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
id_xmatch_api_idhomeTeamIDaway_team_api_idB365HBWHIWHLBHWHHVCHavgOddsid_ycountry_idleague_idseasondate_ybuildUpPlaySpeedClassbuildUpPlayDribblingClassbuildUpPlayPassingClassbuildUpPlayPositioningClasschanceCreationPassingClasschanceCreationCrossingClasschanceCreationShootingClasschanceCreationPositioningClassdefencePressureClassdefenceAggressionClassdefenceTeamWidthClassdefenceDefenderLineClass
id_x1.0000.292-0.087-0.085-0.036-0.022-0.024-0.026-0.028-0.026-0.025-0.0690.8600.8600.2520.0000.2020.1380.1770.2180.1250.1320.1050.1960.1230.0890.1100.241
match_api_id0.2921.000-0.021-0.0260.0210.0450.0360.0540.0580.0540.049-0.0020.0000.0001.0000.0000.0210.0160.0220.0060.0150.0160.0200.0130.0230.0170.0250.014
homeTeamID-0.087-0.0211.000-0.029-0.051-0.054-0.057-0.057-0.054-0.053-0.0550.1920.0590.0590.0780.0200.0410.0240.0210.0110.0240.0140.0270.0180.0320.0180.0090.012
away_team_api_id-0.085-0.026-0.0291.0000.0560.0560.0600.0560.0540.0530.0560.0030.0710.0710.0960.0000.0090.0100.0060.0110.0090.0060.0000.0150.0090.0050.0030.016
B365H-0.0360.021-0.0510.0561.0000.9950.9900.9930.9950.9960.9970.0340.0780.0780.0340.0000.0180.0240.0550.0740.0230.0190.0490.0790.0370.0110.0240.030
BWH-0.0220.045-0.0540.0560.9951.0000.9910.9930.9940.9950.9980.0330.0800.0800.0280.0000.0160.0220.0470.0590.0130.0200.0400.0630.0270.0160.0170.027
IWH-0.0240.036-0.0570.0600.9900.9911.0000.9900.9880.9880.9940.0340.0880.0880.0400.0000.0230.0250.0560.0820.0260.0220.0550.0890.0390.0150.0230.030
LBH-0.0260.054-0.0570.0560.9930.9930.9901.0000.9940.9940.9970.0360.0810.0810.0380.0000.0170.0240.0530.0690.0180.0200.0480.0740.0370.0140.0200.026
WHH-0.0280.058-0.0540.0540.9950.9940.9880.9941.0000.9960.9980.0340.0820.0820.0390.0000.0170.0240.0530.0700.0210.0180.0480.0760.0370.0120.0200.027
VCH-0.0260.054-0.0530.0530.9960.9950.9880.9940.9961.0000.9980.0340.0800.0800.0360.0000.0190.0210.0470.0590.0130.0220.0390.0620.0270.0160.0160.026
avgOdds-0.0250.049-0.0550.0560.9970.9980.9940.9970.9980.9981.0000.0340.0850.0850.0340.0000.0170.0230.0520.0660.0180.0220.0450.0700.0340.0140.0180.030
id_y-0.069-0.0020.1920.0030.0340.0330.0340.0360.0340.0340.0341.0000.3050.3050.0520.0070.1690.1160.1560.2200.1520.1690.1290.2430.1080.1400.1110.147
country_id0.8600.0000.0590.0710.0780.0800.0880.0810.0820.0800.0850.3051.0001.0000.0000.0000.2270.1500.1910.2330.1540.1540.1090.2240.1400.0940.1160.278
league_id0.8600.0000.0590.0710.0780.0800.0880.0810.0820.0800.0850.3051.0001.0000.0000.0000.2270.1500.1910.2330.1540.1540.1090.2240.1400.0940.1160.278
season0.2521.0000.0780.0960.0340.0280.0400.0380.0390.0360.0340.0520.0000.0001.0000.0000.0210.0160.0220.0060.0150.0160.0200.0130.0230.0170.0250.014
date_y0.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0001.0000.1630.6670.2590.2200.2160.1690.2790.1590.3360.3760.3010.421
buildUpPlaySpeedClass0.2020.0210.0410.0090.0180.0160.0230.0170.0170.0190.0170.1690.2270.2270.0210.1631.0000.0690.2850.0910.2480.1870.1220.1590.0890.1970.0490.122
buildUpPlayDribblingClass0.1380.0160.0240.0100.0240.0220.0250.0240.0240.0210.0230.1160.1500.1500.0160.6670.0691.0000.0480.1070.0820.0740.1360.1030.1130.1160.1210.159
buildUpPlayPassingClass0.1770.0220.0210.0060.0550.0470.0560.0530.0530.0470.0520.1560.1910.1910.0220.2590.2850.0481.0000.3260.2490.2270.1500.2740.1910.2100.0960.302
buildUpPlayPositioningClass0.2180.0060.0110.0110.0740.0590.0820.0690.0700.0590.0660.2200.2330.2330.0060.2200.0910.1070.3261.0000.0560.1220.2680.4450.1160.1490.1060.245
chanceCreationPassingClass0.1250.0150.0240.0090.0230.0130.0260.0180.0210.0130.0180.1520.1540.1540.0150.2160.2480.0820.2490.0561.0000.2110.1370.1410.1560.1860.1200.166
chanceCreationCrossingClass0.1320.0160.0140.0060.0190.0200.0220.0200.0180.0220.0220.1690.1540.1540.0160.1690.1870.0740.2270.1220.2111.0000.0690.1900.1050.1430.1180.072
chanceCreationShootingClass0.1050.0200.0270.0000.0490.0400.0550.0480.0480.0390.0450.1290.1090.1090.0200.2790.1220.1360.1500.2680.1370.0691.0000.2420.2130.1160.1320.212
chanceCreationPositioningClass0.1960.0130.0180.0150.0790.0630.0890.0740.0760.0620.0700.2430.2240.2240.0130.1590.1590.1030.2740.4450.1410.1900.2421.0000.0460.0740.0370.189
defencePressureClass0.1230.0230.0320.0090.0370.0270.0390.0370.0370.0270.0340.1080.1400.1400.0230.3360.0890.1130.1910.1160.1560.1050.2130.0461.0000.2310.4350.138
defenceAggressionClass0.0890.0170.0180.0050.0110.0160.0150.0140.0120.0160.0140.1400.0940.0940.0170.3760.1970.1160.2100.1490.1860.1430.1160.0740.2311.0000.1990.368
defenceTeamWidthClass0.1100.0250.0090.0030.0240.0170.0230.0200.0200.0160.0180.1110.1160.1160.0250.3010.0490.1210.0960.1060.1200.1180.1320.0370.4350.1991.0000.156
defenceDefenderLineClass0.2410.0140.0120.0160.0300.0270.0300.0260.0270.0260.0300.1470.2780.2780.0140.4210.1220.1590.3020.2450.1660.0720.2120.1890.1380.3680.1561.000

Missing values

2023-02-28T18:51:53.711891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-28T18:51:54.534075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

id_xcountry_idleague_idseasondate_xmatch_api_idhomeTeamIDaway_team_api_idB365HBWHIWHLBHWHHVCHavgOddsid_ydate_ybuildUpPlaySpeedClassbuildUpPlayDribblingClassbuildUpPlayPassingClassbuildUpPlayPositioningClasschanceCreationPassingClasschanceCreationCrossingClasschanceCreationShootingClasschanceCreationPositioningClassdefencePressureClassdefenceAggressionClassdefenceTeamWidthClassdefenceDefenderLineClass
01729172917292008/20092008-08-17 00:00:0048904210260102611.291.31.31.251.251.281.2768072010-02-22 00:00:00FastLittleMixedOrganisedNormalLotsNormalFree FormMediumPressNormalCover
11729172917292008/20092008-08-17 00:00:0048904210260102611.291.31.31.251.251.281.2768082011-02-22 00:00:00BalancedLittleMixedOrganisedNormalNormalLotsFree FormMediumPressNormalCover
21729172917292008/20092008-08-17 00:00:0048904210260102611.291.31.31.251.251.281.2768092012-02-22 00:00:00BalancedLittleMixedOrganisedNormalNormalNormalOrganisedMediumPressNormalCover
31729172917292008/20092008-08-17 00:00:0048904210260102611.291.31.31.251.251.281.2768102013-09-20 00:00:00BalancedLittleMixedOrganisedNormalLotsNormalOrganisedMediumPressNormalCover
41729172917292008/20092008-08-17 00:00:0048904210260102611.291.31.31.251.251.281.2768112014-09-19 00:00:00BalancedNormalMixedOrganisedNormalLotsNormalOrganisedMediumPressNormalCover
51729172917292008/20092008-08-17 00:00:0048904210260102611.291.31.31.251.251.281.2768122015-09-10 00:00:00BalancedNormalMixedOrganisedNormalNormalNormalOrganisedMediumPressNormalCover
61739172917292008/20092008-10-29 00:00:004891321026086541.201.21.21.201.201.201.2008072010-02-22 00:00:00FastLittleMixedOrganisedNormalLotsNormalFree FormMediumPressNormalCover
71739172917292008/20092008-10-29 00:00:004891321026086541.201.21.21.201.201.201.2008082011-02-22 00:00:00BalancedLittleMixedOrganisedNormalNormalLotsFree FormMediumPressNormalCover
81739172917292008/20092008-10-29 00:00:004891321026086541.201.21.21.201.201.201.2008092012-02-22 00:00:00BalancedLittleMixedOrganisedNormalNormalNormalOrganisedMediumPressNormalCover
91739172917292008/20092008-10-29 00:00:004891321026086541.201.21.21.201.201.201.2008102013-09-20 00:00:00BalancedLittleMixedOrganisedNormalLotsNormalOrganisedMediumPressNormalCover
id_xcountry_idleague_idseasondate_xmatch_api_idhomeTeamIDaway_team_api_idB365HBWHIWHLBHWHHVCHavgOddsid_ydate_ybuildUpPlaySpeedClassbuildUpPlayDribblingClassbuildUpPlayPassingClassbuildUpPlayPositioningClasschanceCreationPassingClasschanceCreationCrossingClasschanceCreationShootingClasschanceCreationPositioningClassdefencePressureClassdefenceAggressionClassdefenceTeamWidthClassdefenceDefenderLineClass
851122452921518215182015/20162015-10-03 00:00:002030143830683722.12.102.12.02.12.052.076682012-02-22 00:00:00BalancedLittleMixedOrganisedNormalNormalNormalOrganisedMediumPressNormalCover
851132452921518215182015/20162015-10-03 00:00:002030143830683722.12.102.12.02.12.052.076692013-09-20 00:00:00BalancedLittleMixedOrganisedNormalNormalNormalOrganisedMediumPressNormalCover
851142452921518215182015/20162015-10-03 00:00:002030143830683722.12.102.12.02.12.052.076702014-09-19 00:00:00BalancedNormalMixedOrganisedNormalNormalNormalOrganisedMediumPressNormalCover
851152452921518215182015/20162015-10-03 00:00:002030143830683722.12.102.12.02.12.052.076712015-09-10 00:00:00BalancedNormalMixedOrganisedNormalNormalNormalOrganisedMediumPressNormalCover
851162455021518215182015/20162015-10-25 00:00:0020301648306102053.53.253.33.53.53.603.436662010-02-22 00:00:00SlowLittleShortOrganisedNormalNormalLotsOrganisedMediumPressNormalOffside Trap
851172455021518215182015/20162015-10-25 00:00:0020301648306102053.53.253.33.53.53.603.436672011-02-22 00:00:00BalancedLittleMixedOrganisedRiskyNormalNormalOrganisedMediumPressWideCover
851182455021518215182015/20162015-10-25 00:00:0020301648306102053.53.253.33.53.53.603.436682012-02-22 00:00:00BalancedLittleMixedOrganisedNormalNormalNormalOrganisedMediumPressNormalCover
851192455021518215182015/20162015-10-25 00:00:0020301648306102053.53.253.33.53.53.603.436692013-09-20 00:00:00BalancedLittleMixedOrganisedNormalNormalNormalOrganisedMediumPressNormalCover
851202455021518215182015/20162015-10-25 00:00:0020301648306102053.53.253.33.53.53.603.436702014-09-19 00:00:00BalancedNormalMixedOrganisedNormalNormalNormalOrganisedMediumPressNormalCover
851212455021518215182015/20162015-10-25 00:00:0020301648306102053.53.253.33.53.53.603.436712015-09-10 00:00:00BalancedNormalMixedOrganisedNormalNormalNormalOrganisedMediumPressNormalCover